A survey on image mosaicing techniques

Image Mosaicing is a method of assembling multiple overlapping images of the same scene into a larger image. The output of the image mosaic will be the union of two input images. Image-mosaicing algorithms are used for obtaining a mosaiced image. There are five steps used in image mosaicing which includes; Feature extraction, Image registration, Computing homography, Warping and Blending. Feature extraction is an Image mosaicing technique which is done by using various corner detection algorithm. This corner detection algorithm produces an efficient and informative output mosaiced image. Importance of Image mosaicing can be seen in the field of medical imaging, computer vision, data from satellites, military automatic target recognition. Index Terms — Image Registration, Warping ,Corner, Homography, Blending. I.INTRODUCTION Image mosaicing is the stitching of multiple correlated images to generate a larger wide-angle image of a scene. Mosaicing could be regarded as a special case of scene reconstruction where the images are related to planar homography only. An Image Mosaic is a synthetic composition generated from a sequence of images and it can be obtained by understanding geometric relationships between images. The geometric relations are the coordinate systems that relates the different image coordinate systems. The appropriate transformations is applied via of a warping operation than by merging the overlapping regions of warped images. In image mosaicing two input images are taken and this images are fused to form a single large image. This merged single image is the output mosaiced image. The first step in Image Mosaicing is feature extraction. In feature extraction, features are detected in both input images. Image registration refers to the geometric alignment of a set of images. The different sets of data may consist of two or more digital images taken of a single scene from different sensors at different time or from different viewpoints. In image registration the geometric correspondence between the images is established so that they may be transformed, compared and analyzed in a common reference frame. This is of practical importance in many fields, including remote sensing, computer vision, medical imaging. Registration methods can be loosely divided into the following classes: algorithms that use image pixel values directly, e.g., correlation methods [2];algorithms that use the frequency domain, e.g., Fast Fourier transform based (FFT-based) methods [3];algorithms that use low level features such as edges and corners, e.g., Feature based methods [4];and algorithms that use high-level features such as identified parts of image objects, relations between image features, for e.g., Graph-theoretic methods[4].The next step, following registration, is image warping which includes correcting distorted images and it can also be used for creative purposes. The images are placed appropriately on the bigger canvas using registration transformations to get the output mosaiced image. The quality of the mosaiced image and the time efficiency of the algorithm used are given most importance in image mosaicing. Image Blending is the technique which modifies the image gray levels in the vicinity of a boundary to obtain a smooth transition between images by removing these seams and creating a blended image. Blend modes are used to blend two layers into each other. A. Image Mosaicing Model II.FEATURE EXTRACTION The first step in image mosaic process is feature detection. Features are the elements in the two input images to be matched. For images to be matched they are taken inside an